Printing devices are typically configured with a set of colorants, e.g. inks, pigments, and the like. The term “process colorants” usually refers to a set of colorants including three chromatic colorants (e.g. cyan, magenta, and yellow) and an achromatic colorant (e.g. black) because a wide range of target colors can be obtained by printing combinations of these colorants, each with varying tonality.
Some printing devices can be configured with additional colorants for various reasons. In some situations, process colorants with lighter or darker densities can be added to improve the colorant tonal granularity and/or the printer gamut and/or smoothness in tonal transitions. In other situations, additional process colorants (e.g. orange, green, and blue) can be added to increase the printer gamut. In still other situations, spot colorants can be added to achieve a specific effect, such as to exactly match a target color or to provide a desired effect (e.g. gloss).
Use of additional spot colorants to match a target color is particularly widespread in the printing of commercial product packaging where brand color accuracy is crucial. However, the use of spot colorants increases the complexity and costs associated with printing equipment and processes. It is desirable to minimize the use of spot colorants but there are some barriers to the use of process colorant recipes for target colors.
For some target colors, the printer gamut may not be large enough to enable the target color to be printed. This can usually be overcome by changing the process colorant formulations (e.g. more vivid colorants) or by adding process colorants to extend the printer gamut (e.g. Hexachrome® colorant set).
For target colors that lie within the printer gamut, prior art methods and systems for characterizing a printing device color response can be a barrier to determining accurate target color recipes. One problem relates to the granularity of test data used for characterization of the printer. In general, characterization chart patches are relatively sparsely distributed throughout colorant space. A primary reason for this is to reduce the number of samples that must be measured.
Once test patches have been measured a color model is typically created so that a device-independent color (e.g. CIELAB coordinate) can be predicted for an arbitrary combination of colorant tint values, and similarly a colorant combination can be predicted to achieve a target color. Use of parametric models and interpolation between measured values is known in the art for mapping between colorant and color coordinates that do not have a measurement basis. However, color modeling techniques can produce poor target color recipes due to the coarseness of the test data and nonlinear characteristics of a printer's color response.
Standard charts are widely employed in commercial printing. Two exemplary standards include the IT8 standard specified by the American National Standards Institute and the ECI2002 standard specified by the European Color Initiative. Many of the test patches defined by the newer versions of both standards have identical colorant combinations.
The IT8 standard provides a range of recommendations. For example, the IT8.7/3 recommendation defines the use of 182 color patches (basic set) and 982 color patches (extended set) to characterize a printer. The newer IT8.7/4 recommendation defines the use of 1617 color patches (with some replicates) to provide a more fine-grained characterization. The 1588 unique IT8.7/4 patches can be categorized as: single colorant patches (84), two colorant combination patches (288), three colorant combinations (818), and four colorant combinations (398). Single colorant patches provide the finest granularity of sampling with 21 somewhat uniform tonality steps.
Two colorant patches can be further categorized into purely chromatic patches (204) and those including black colorant (84). Approximately 8 tone steps (8 steps*8 steps*3 combinations=192) are used for each colorant in the purely chromatic patches. Approximately 5 tone steps (5*5*3=75) are used for each colorant in the two colorant patches including black colorant. The actual number of tone steps for a colorant varies somewhat dependent on the colorant and the position of the associated colorant combination within the colorant space of the printer. For example, some additional tone steps are added in the highlights for some colorants.
Three colorant patches have distribution granularity similar to two colorant patches with 527 purely chromatic patches and 84 patches that include black colorant. Approximately 8 tone steps (8*8*8=512) are used for each colorant in purely chromatic patches. Approximately 5 tone steps (5*5*5*3=375) are used for each colorant in the three colorant patches including black colorant.
Four colorant patches use approximately 4-5 tone steps (4*4*5*5=400) for each colorant. Thus, it is clear to see that the granularity of colorant combination patches for the newer standard test charts is quite large and the color model data derived from measuring these patches can be a problem when determining target color recipes.
Having two few samples in nonlinear regions of a printer's gamut can increase the error in color accuracy prediction. Further, different regions of a printer's gamut can have very different nonlinear characteristics making the use of parametric models difficult to provide the desired accuracy with coarse characterization data.
Some prior art systems overcome these limitations by developing separate localized models in the region corresponding to a desired target color. One example includes U.S. Publication No. 2008/0130022 (Dalal et al.). Dalal et al. teaches developing a set of color models, one for general use throughout the gamut and one for each region of interest such as near a target color. An initial target color recipe is printed and measured. Dalal et al. teaches developing a local model in the vicinity of the color measured for the initial recipe with the model using a weighting of the neighboring color measurements to compensate for local nonlinearities. This method can be very time consuming and complex when a significant number of target colors is desired (e.g. some Pantone® libraries specify between 1000 and 3000 colors).
Other examples include U.S. Publication No. 2009/0296113 (Mestha et al.) and U.S. Publication No. 2008/0043271 (Gil et al.). Mestha et al. teaches a similar approach where the error of a color reproduced for an initial target recipe is reduced by selecting a linear gain matrix that models the local color response of the printer. U.S. Pat. No. 6,919,972 (Kumada et al.) teaches a similar approach with target color interpolation improved by localized weighting of neighboring measured data.
Some prior art teaches improving the accuracy of target color recipes by utilizing a larger set of test patches. For example, an extremely fine grained characterization of the printer gamut would enable traditional modeling techniques to overcome many of the nonlinear color response prediction errors. However, this is widely viewed as impractical because of increased measurement and computing resources requirements. A similar example includes U.S. Publication No. 2010/0189348 (Dalal et al.), which teaches using two sets of characterization measurements separated by time (and presumably by drift in response) to adjust an initial target color recipe based on the two sets of characterization data.
Other prior art systems teach different approaches. One example is U.S. Publication No. 2009/0251712 (Wang et al.). Wang et al. teaches using a minimal number of patches (less than 20) for each of a set of sub-gamuts (e.g. CMY, MYK, CYK, and CMK) to build a linear sensitivity model of each sub-gamut's color to changes in the corresponding colorant coordinates. Wang et al. does not appear to teach how such a collection of sub-gamut sensitivity models can be used to identify a target color recipe.
A need exists, therefore, to reduce the time and resources required for characterizing a printer and developing accurate target color recipes for that printer.